Link Traffic Flows Research Articles

A Signal Coordination Model for Long Arterials Considering Link Traffic Flow Characteristics

In order to improve the travel efficiency on a long arterial with massive signals, this paper proposed a mixed integer linear programming model, MaxBandLAM, to simultaneously optimize the arterial partition scheme and signal coordination schemes with the consideration of link traffic flow characteristics. The weighted sum of the two-way green bandwidths on links across the arterial was taken as its objective. The link volume to capacity rations were taken as the weights. The number of sub-zones, the allocation of signals and links, signal cycle, offset and phase sequence, the coordination speed, as well as the two-way green bandwidths of sub-zones and of links are the decision variables. A numerical test was carried out on a virtual arterial with twenty signals. The results indicate the scheme generated by the MaxBandLAM model can provide a more reasonable partition scheme and coordination schemes compared to those generated by Synchro and the models with no traffic flow characteristics of links consideration. For the scenario of dominating the main road through flows, the average delay, average stop number, and average travel speed at intersections for all-turning flows and main road through flows in the MaxBandLAM scheme, all performed well.

Analasis Penerapan Teknologi Traffic Steering SD-WAN Menggunakan Perangkat FortiGate

In the current digital transformation era, network providers have long relied on WAN technology to support the business communications of their multi-local companies; however, in terms of infrastructure, cost, and even effectiveness, WAN technology is no longer sufficient to meet the needs and demands of telecommunications customers. In addition, given the magnitude of the company's need for internet network connections, technology is also needed to manage the use of traffic from rental links in the company so that it can optimize bandwidth usage from the link itself. Therefore one of the technological innovations to improve information processes is cloud-based digital innovation such as SD-WAN Traffic Steering technology. In this paper, the SD-WAN network design will be carried out on FortiGate series 50e devices by implementing traffic steering technology which can adjust a link's traffic flow according to the client's wishes. The results of the data obtained indicate that the utilization of ISP links owned by companies (Lintasarta and MNC) has been able to be used optimally, as evidenced by the use of bandwidth on each link successfully used according to the desired settings, where the settings provided have also been adjusted according to parameters of the type of each ISP service so that functionally the existing service link can be used optimally by the compan

Multi-objective optimization of cordon sanitaire considering operation cost and queueing equity

ABSTRACT This paper aims topropose a method to determine the optimal deployment of cordon sanitaire for epidemic control. A bi-level multi-objective programming model is proposed where the lower level is transportation system equilibrium with queueing time to predict link traffic flow, and the upper level is queueing network design, which is a multi-objective integer nonlinear programming model. The primary objective is to minimize the total operation cost of servers with a predetermined waiting time constraint, and the secondary objective is to reach queueing equity. A heuristic algorithm is designed where the method of successive averages is adopted for the lower-level model, and a genetic algorithm is designed for the upper-level model. The computational results show that the methods can find a good heuristic optimal solution. These methods are useful for planners to determine the optimal deployment of cordon sanitaire for disease control and prevention purposes.

Structured nonnegative matrix factorization for traffic flow estimation of large cloud networks

Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link traffic flows, Y, and a binary routing matrix, A, which are such that Y=AX. This is a challenging but vital problem as accurate estimation of OD flows is required for several network management tasks. In this paper, we propose a novel model for the network traffic matrix estimation problem which maps high-dimension OD flows to low-dimension latent flows with the following three constraints: (1) nonnegativity constraint on the estimated OD flows, (2) autoregression constraint that enables the proposed model to effectively capture temporal patterns of the OD flows, and (3) orthogonality constraint that ensures the mapping between low-dimensional latent flows and the corresponding link flows to be distance preserving. The parameters of the proposed model are estimated with a training algorithm based on Nesterov accelerated gradient and generally shows fast convergence. We validate the proposed traffic flow estimation model on two real backbone IP network datasets, namely Internet2 and GÉANT. Empirical results show that the proposed model outperforms the state-of-the-art models not only in terms of tracking the individual OD flows but also in terms of standard performance metrics. The proposed model is also found to be highly scalable compared to the existing state-of-the-art approaches.

Identification of critical links in a large-scale road network considering the traffic flow betweenness index.

The traditional full-scan method is commonly used for identifying critical links in road networks. This method simulates each link to be closed iteratively and measures its impact on the efficiency of the whole network. It can accurately identify critical links. However, in this method, traffic assignments are conducted under all scenarios of link disruption, making this process prohibitively time-consuming for large-scale road networks. This paper proposes an approach considering the traffic flow betweenness index (TFBI) to identify critical links, which can significantly reduce the computational burden compared with the traditional full-scan method. The TFBI consists of two parts: traffic flow betweenness and endpoint origin–destination (OD) demand (rerouted travel demand). There is a weight coefficient between these two parts. Traffic flow betweenness is established by considering the shortest travel-time path betweenness, link traffic flow and total OD demand. The proposed approach consists of the following main steps. First, a sample road network is selected to calibrate the weight coefficient between traffic flow betweenness and endpoint OD demand in the TFBI using the network robustness index. This index calculates changes in the whole-system travel time due to each link’s closure under the traditional full-scan method. Then, candidate critical links are pre-selected according to the TFBI value of each link. Finally, a given number of real critical links are identified from the candidate critical links using the traditional full-scan method. The applicability and computational efficiency of the TFBI-based approach are demonstrated for the road network in Changchun, China.

A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

Abstract Short-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

Strategic Environmental Assessment with the Penetration of Electric Vehicles in Transport Network

The ownership of electric vehicles, including plug-in electric vehicles (PEVs) and hybrid electric vehicles (HEVs), begins to be prevalent at present and poses as a promising way to alleviate environmental pollutions. However, the traditional method of environmental impact assessment is encountering challenges due to the large differences between conventional internal combustion engine vehicles (ICEVs) and electric vehicles. This paper proposes a novel SEA method for the penetration of electric vehicles at link level. An absorbing Markov chain model is formulated to represent the transition process among ICEVs, PEVs, and HEVs in the future, where PEV is an absorbing state. In addition, both HEV and PEV travelers are assumed to behave in the same manner as conventional ICEV travelers. As a result, the vehicle fleet composition is consistent with their market shares on each link. However, the three vehicle types are distinguished by their respective emissions rates. Therefore, the strategic environmental assessment can be worked out based on market penetration level and link traffic flows. For a given road network, transport system equilibrium is proposed to calculate link traffic flows. It is a four-step sequential model with feedback channel which can be solved by method of successive averages (MSA) effectively. For sustainable development, China plans to build a new city named Xiong’an nearby the capital Beijing. The strategic environmental assessment is conducted using the development of Xiong’an as an experimental study to demonstrate the effectiveness of proposed method. This research is very useful in urban planning with environmental constraints.

Shortest paths, travel costs, and traffic

This study examines path flows for road networks. Path flows sum individual route choices from individual travelers, associated with specific path objective. We estimate these flows for each cost factor of auto travel: time, safety, emission, and monetary costs, as well as their composite, internal and full cost of travel. For each factor, we find the route with the minimum cumulative cost. We further explore the extent to which each cost factor contributes to explaining the observed link traffic flows given an estimated home-to-work demand pattern. The results of the Minneapolis – St. Paul metropolitan area indicate that flows from multiple path types, associated with different internal cost components, along with distance, provide the best fit.

Incorporating multi-level taste heterogeneity in route choice modeling: From disaggregated behavior analysis to aggregated network loading

The aggregated traffic flow forecasting problem with multi-level mixed logit based route choice model is discussed in this paper. Multi-level unobserved taste heterogeneity in route choice is further divided to two parts, the OD pair specific and observation specific. With the proposed model, the observations between the same OD pair, as well as the random utilities between alternative routes are correlated because of sharing the same OD pair specific taste heterogeneity. According to the theoretical analysis, these correlations result in the forecasting errors of aggregated link traffic flow that cannot be ignored as the increasing of number of trips. Numerical studies are carried out to illustrate the quantitative effects of incorporating multi-level taste heterogeneity on disaggregated route choice prediction and aggregated network loading, as well as the effects of model mis-specification on parameter recovery and prediction performance.

Traffic flow estimation on the expressway network using toll ticket data

Traffic flow estimation (TFE) is an important task for intelligent transportation systems, especially for expressway system in travel demand management, road conditions monitor etc. Traffic flow is usually detected by fixed sensors, which is neither practical nor economically feasible to cover all expressway component links. Additionally, the existing research usually ignores the effects of drivers’ dwell behaviours on TFE. To fill these gaps, this study proposes a TFE method based on toll ticket data (TTD) which records trip information, except the actual route. Meanwhile, dwell time, as a key component of drivers’ journeys, will be integrated into the proposed approach. In this study, a Gaussian mixture model for the origin–destination pairs with multiple alternative routes will be formulated. Moreover, expectation–maximisation algorithm is introduced to estimate link traffic flows and dwell time distributions simultaneously. By TTD collected from the Shandong Province Expressway in China, the proposed approach is calibrated and applied to TFE empirically. The results show that the proposed approach has a good performance on TFE with consideration of dwell time.

基于含权Newman算法的交通控制子区划分

为使交通网络控制子区内的交通流具有更好的同质性，本文将道路网络中的路段抽象为点，相邻路段之间的连接关系抽象为边，形成一个对偶的网络拓扑结构图。以用户均衡交通分配得到的路段交通流数据除以路段长度计算得到路段的“拟交通密度”；通过引入路段拟交通密度，扩展了Newman子区划分算法。最后，选取实际路网、以NSK (Normalized cut Silhouette)指标验证K均值聚类算法、传统Newman算法和扩展的Newman子区划分算法的优劣。研究发现：传统K均值聚类算法得到的各子区NSK指标和路网NSK指标都相对最小，但同一个子区内的路段在空间位置上不相连，没有实际运用价值；扩展的Newman子区划分算法的NSK值优于传统Newman快速划分算法，证实引入了路段拟交通密度作为边权，使得划分出来的结果更加符合交通网络的特性。 A dual network is structured firstly by setting link of road network as dual node and incidence relationship between links of road network as dual links in the dual network in order to keep homogeneity of traffic flow in each road sub-network partitioned. Link traffic flow, which is the result of traffic demand assignment based on user equilibrium principle, is divided by link length and then the definition of quasi traffic density of each link is proposed secondly. Newman network partition algorithm is extended by introducing the definition of quasi traffic density. Thirdly, the superiority of K-means cluster algorithm, Newman network partition algorithm and extended Newman network partition algorithm are identified by measurement of Normalized cut Silhouette (NSK) on a real road network. This study has identified that: indexes value NSK and NSK on networks of traditional K-means cluster algorithm are relatively small, but the sub-network partitioned is not connected in the space so that it lacks practical value. Index NSK of extended Newman network partition algorithm is prior to that of Newman network partition algorithm. The sub-network which is partitioned by extended Newman network partition algorithm introduced the definition of quasi traffic density is favorable for purpose of traffic flow control.

Simulation-based pricing optimization for improving network-wide travel time reliability

ABSTRACTTravel time variability in a network is an important measure of the transportation system performance and a major factor influencing the decision-making of system management such as congestion pricing. The congestion-pricing problem with reliability maximization as the goal of a transportation network is characterized by expensive-to-evaluate objective functions without closed forms. In this paper, an effective simulation-based optimization (SBO) method is utilized to solve the problem. The network reliability is measured by a weighted average link travel time coefficient of variation (CV) with link traffic flows as the weights. We employ DynusT to evaluate the system reliability as the objective function corresponding to different toll charges for a new toll road in Maryland. The results show that the two optimal toll charge strategies improve the network-wide reliability, reducing the weighted travel time CV by 9.60% and 1.16%, respectively, when compared to the baseline toll.

Application of the Conditional Inverse Matrix Theory for Estimation of Origin-Destination Trip Matrices from Link Traffic Flows

Application of the Conditional Inverse Matrix Theory for Estimation of Origin-Destination Trip Matrices from Link Traffic Flows

Dynamical evolution processes of traffic flow and travel cost in urban transportation networks

Considering such a fact that travellers dynamically adjust their routes and the resultant link traffic flows in a network evolve over time, this paper proposes a dynamical evolutionary model of the traffic assignment problem with endogenous origin—destination (OD) demands. The model's stability is analysed and the resultant user equilibrium (UE) state is shown to be stable under certain conditions. Numerical results in a grid network indicate that the model can generate convergent flow patterns and finally terminates at the UE state. Impacts by the parameters associated with OD demand function and link cost function are also investigated.

A composite traffic flow modeling approach for incident-responsive network traffic assignment

This paper presents a hybrid traffic simulation-based model to address the network traffic route choice issue under conditions of lane-blocking incidents on surface streets. The proposed approach includes four sequential mechanisms: (1) link flow loading, (2) link traffic moving, (3) link cost calculation, and (4) searching the shortest path. To deal with the traffic flows moving on lane-blocking links, specific incident-induced link traffic flow models, which are extended from the Lighthill–Whitham (L–W for short) model, are formulated. A simulation-based approach is then proposed to determine the instantaneous shortest path associated with each vehicle approaching to each given intersection on the network. In addition, numerical examples associated with diverse incident scenarios are investigated. The numerical results demonstrate the competitiveness of the proposed simulation-based method by reducing the network-wide path travel time by 11.4% and the incident impact on link traffic flows by 66.7% in comparison with the Paramics traffic simulator. It is expected that this study can provide linkage between the fields of incident management and dynamic traffic assignment that will allow the development of such related technologies as real-time incident-responsive route guidance and incident management systems.

Some Consistency Conditions for Dynamic Traffic Assignment Problems

This paper explores some critical issues in modeling network traffic flow for predictive user optimal dynamic traffic assignment. They include the role of FIFO (first-in-first-out) in link, path and network traffic dynamics, minimal set representation of link traffic evolution, and consistency conditions for link models. It is found that (1) link FIFO plays a central role in modeling link, path and network traffic flow evolution, and (2) the dimension of a minimal state set that adequately describes link traffic flow under FIFO is two. Moreover, two sufficient FIFO conditions are provided, and pitfalls in using these conditions to enforce FIFO are pointed out. Finally, ramifications of these findings to DTA modeling are also discussed.

Calibration and Validation of a Dynamic Traffic Assignment Model

A new approach is proposed to calibrate and validate the most critical components of a dynamic traffic assignment (DTA) model: dynamic route choice and flow propagation. By presenting approximate joint probability distribution functions of the temporal link traffic flows on a network, it is possible to derive the likelihood functions for estimating dynamic route choice and actual flow propagation. This approach also enables statistical tests to be performed for validation of DTA models. Both procedures are presented with a small numerical example and a larger network. These examples also indicate that it is possible to calibrate and validate a DTA model with detection errors and incomplete data, especially when real-time traffic counts are available on only a few links in the network.

PREDICTIVE AND DYNAMIC USER OPTIMAL TRAFFIC ASSIGNMENT AS A TIME-DELAY OPTIMAL CONTROL PROBLEM

This paper presents a look at dynamic user optimal traffic assignment as a time-delay optimal control problem. The approach proposed here allows us to develop a model for solving the predictive and dynamic user optimal assignment problem which is capable for considering both time-delay effects in the dynamic of link traffic flow and the information providing process to travelers.

Classification of Road Network Function in Local City Based on OD Proportion of Link Traffic Flow

Classification of Road Network Function in Local City Based on OD Proportion of Link Traffic Flow

Some statistical problems in connection with traffic assignment

This paper gives the approximation joint probability distribution function of the link traffic flows on a network. The knowledge of such distribution enables us to derive the log-likehood function for the estimation (with link data) of the traffic diversion parameter, Ф, that appears in most stochastic assignment models. It also enables us to perform goodness to fit statistical tests for the validation of traffic assignment methods. Both of these procedures are illustrated with a small numerical example. It is also pointed out that it is possible to calibrate and/or validate a traffic assignment model with incomplete data base (i.e. when traffic counts are only available on a few of the links of the networks).